We thank Marek Weiler for performing the phantom experiments. This work was supported by the European Research Council (ERC Starting Grant 309495: NeoNaNo), the German Federal State of North Rhine Westphalia (NRW; High-Tech.NRW/EU-Ziel 2-Programm (EFRE); ForSaTum), the German Ministry for Education and Research (BMBF) (funding programs Virtual Liver (0315743), LungSys (0315415C), LungSys2(0316042F), Photonik Forschung Deutschland (13N13355)), the RWTH Aachen University (I3TM Seed Fund), and Philips Research (Aachen, Germany).

The protocol contains a detailed description of the following steps: At first, phantoms or mice and the multimodal mouse bed are prepared for imaging. Then a whole-body scan is acquired in the &#181;CT. Subsequently the mouse bed is transferred to the FMT where two scans are acquired (up and upside down). This can be repeated for multiple mice at multiple time points. After completion of the data acquisition, the data needs to be exported and sorted to enable automated segmentation (requiring a Definiens software license), as well as image fusion and fluorescence reconstruction (requiring an Imalytics Preclinical software license). Finally it is shown how the multimodal data sets are visualized and how organs are interactively segmented to quantify the biodistribution of fluorescent probes.
1. Phantom Preparation
NOTE: Phantoms are useful to test the imaging system, but also to determine the calibration factor for a new probe.
<ol>
	<li>Prepare a solution of 200 ml water, 2 % agarose, 1.8 g TiO2 powder, 50 &#181;l trypan blue. After boiling, fill the solution into a rectangular form, approximately of 8 cm length, 3 cm width, and 1.5 cm height.</li>
	<li>Prepare several fluorescent inclusions in the phantom using pipette tips, containing a mix of fluorescence and &#181;CT contrast agent. To create the inclusions, cut the pipette tips and seal them with a lighter.</li>
	<li>After the solution has solidified, insert the inclusions into the phantom. Cut away some parts of the phantom to achieve an irregular shape and to fit it to the multimodal mouse holder.</li>
	<li>To determine the calibration factor for a new probe, some phantom scans are required. For this, the default FMT phantom is used in combination with known amounts of the probe. For an increased accuracy, add 4 % lipid emulsion to the solution to receive the same scattering coefficient inside the inclusion as in the rest of the phantom. Also add a small amount (2 %) of &#181;CT contrast agent for easier image analysis.</li>
</ol>
2. Mouse Preparation
NOTE: &#181;CT-FMT imaging requires special preparation including anesthetization and hair removal.
<ol>
	<li>Place the mouse on chlorophyll-free food 7 days before imaging. This will reduce the background signal and is particularly important for FMT channels below 750 nm.</li>
	<li>All animal experiments are performed under anesthesia. To initiate the anesthesia, place the mouse in a chamber filled with 2 % isoflurane in air (flow rate 5 L/min) until the mouse is falling asleep. Confirm proper anesthetization by gentle toe or skin pinching and by checking relaxation of muscle tonus (e.g., jaw muscle). To sustain the anesthesia, continue the isoflurane application using a tube that is placed on the nose of the mouse (2 % isoflurane in air, flow rate 1 L/min). In order to prevent eye dryness, use vet ointment on anesthetized mice.</li>
	<li>To inject contrast agent, fix the anesthetized mouse on a heating pad using tape. Place a catheter (syringe needle attached to a tube) to the tail vein and inject the fluorescent contrast agent (e.g., 2 nmol, with maximal injection volume of 5 ml/kg body weight, i.e., 150 &#181;l for a 30 g mouse).</li>
	<li>For scanning a hairy mouse, the scanning area has to be depilated in advance. For this, use a shaver or hair removal cream. Some strains of mice can develop rashes from the hair removal cream. Therefore, monitor the mice for skin changes and contact veterinary staff for care if needed. Also test the tolerance on a small number of animals when using new mouse strains.</li>
	<li>Keep the mouse anesthetized during &#181;CT and FMT imaging (2 % isoflurane in air, flow rate 1 L/min).</li>
</ol>
3. Mouse Bed Preparation
NOTE: For &#181;CT-FMT scanning, use a multimodal mouse bed, which fits both into the &#181;CT and the FMT.
<ol>
	<li>Before imaging, clean the mouse bed with wet tissues. Do not use ethanol because this may damage the acrylic glass. Make sure that the markers are free of water, because this may impair the automated marker detection.</li>
	<li>Open the screws of the multimodal mouse bed and remove the upper part.</li>
	<li>Attach the anesthetic gas tube on the mouse bed and fixate it with tape.</li>
	<li>Place the anesthetized mouse into the mouse bed and put the nose into the gas tube. Make sure the head of the mouse is at the front indicator of the mouse bed (Figure&#160;1).</li>
	<li>Make sure that the mouse is in the middle of the mouse bed to optimally use the field of view of the FMT.</li>
	<li>Close the mouse bed and tighten the screws until the mouse is tightly held. Make sure the mouse can breathe steadily by visual monitoring of thoracic breathing movements.</li>
</ol>
4. &#181;CT Imaging
NOTE: A whole-body scan is performed using the &#181;CT. The generated anatomical data is required for image fusion, for an improved fluorescence reconstruction and for image analysis.
<ol>
	<li>Place the mouse bed with the mouse into the &#181;CT. Make sure that the mouse is placed in a way that it goes &#8220;tail-first&#8221; into the &#181;CT. This is important for the automated fusion.</li>
	<li>In order to sustain anesthesia when the &#181;CT-lid is closed, reconnect the tubes to channel the gas through the case of the &#181;CT. First detach the long tube from the mouse bed and attach it to the connector at the outside of the &#181;CT. Then attach the remaining free end to the connector inside the &#181;CT.</li>
	<li>Drive the mouse bed into the &#181;CT. Make sure that the gas tube is not loose and cannot get caught by the rotating gantry. If necessary, fixate it with tape. Insert the tube into the cut-out of the holder of the mouse bed.</li>
	<li>Close the &#181;CT and acquire a topogram. Select at least two subscans to cover a substantial part of the mouse and the mouse bed, which is important for the fusion and reconstruction.</li>
	<li>Select the &#181;CT scan protocol named HQD-6565-360-90, which acquires 720 projections with 1032 x 1012 pixels during one full rotation requiring a scanning time of 90 s per subscan. Tubes are operated at voltage 65 kV and current 1.0 mA. Alternatively, to reduce the radiation dose and scanning duration, select the scanning protocol SQD-6565-360-29 which acquires 720 projections with 516 x 506 pixels with scanning time 29 s per subscan.</li>
	<li>Start the &#181;CT scan. The blue bar indicates the progress. The subscans will be acquired subsequently. Hypothermia and fluid loss are not a problem because of the short scanning duration of only a few minutes. Do not open the lid of the &#181;CT during scanning because this will automatically interrupt the scanning to protect the user from radiation.</li>
	<li>When the scanning is completed, open the lid, reconnect the anesthetic tube and detach the mouse bed from the holder to transport it to the FMT.</li>
</ol>
5. FMT Imaging
NOTE: Directly after &#181;CT scanning, the mouse is scanned in the FMT in two configurations (up and upside down) which are used together for an improved fluorescence reconstruction.
<ol>
	<li>Turn on the anesthetic gas supply (2 % isoflurane in air, flow rate 1 L/min) for the FMT before placing the mouse bed into the FMT. Using the FMT control software, create a study group with an appropriate number of subjects (i.e., mice). Select the probes that will be used for imaging (use OsteoSense for novel probes that are not listed).</li>
	<li>Carry the multimodal mouse bed with the mouse to the FMT. The long flexible anesthetic gas tube maintains the gas flow. Before inserting the mouse bed into the FMT, carefully remove the tube since it is not needed inside the FMT. Avoid turning the screws of the mouse bed.</li>
	<li>Place the mouse bed into the FMT with the red indicator first (&#8220;head-first&#8221;). This is important for the image fusion to be consistent with the &#181;CT.</li>
	<li>Close the FMT.</li>
	<li>Select the correct study group and subject. Select the required channel of the FMT (for OsteoSense 750EX, use the 750 nm channel).</li>
	<li>Add a description, e.g., &#8220;up&#8221; or &#8220;down&#8221; and acquire an overview scan by pressing &#8220;Capture&#8221;. This captures a reflectance image of the entire field of view. Make sure to not have &#8220;Reflectance Images Only&#8221; selected, because otherwise it is not possible to acquire 3D scans subsequently.</li>
	<li>Adjust the imaging parameters for the 3D scan. Enlarge the field of view to include as much as possible of the mouse. Usually, the head and tail will not entirely fit into the field of view, however. Click &#8220;Advanced&#8221; and check the imaging settings. Set sampling density to 3 mm, sensitivity to normal and illumination min/max to 5000 and 50000, respectively.</li>
	<li>Click &#8220;add to reconstruction queue&#8221; and then click &#8220;Scan&#8221; to start the FMT scan. This will take around 5 to 15 min, depending on the size and thickness of the mouse, because longer exposure times are required for thicker objects. The device contains a heated imaging chamber to avoid hypothermia.</li>
	<li>After the scan, flip the mouse bed including the mouse upside down and acquire another scan. This provides additional data for the fluorescence reconstruction.</li>
	<li>When the &#181;CT and the FMT-scans are completed and the mouse wakes up from the anesthesia, do not leave it unattended until it has regained sufficient consciousness, e.g., to walk around or to maintain sternal recumbency.</li>
</ol>
6. Image Fusion and Reconstruction
NOTE: After completion of the &#181;CT-FMT scanning, e.g., at the end of the study, the acquired data needs to be sorted to enable the automated image fusion and fluorescence reconstruction.
<ol>
	<li>To sort the scans for further processing, create a folder for the study. For each &#181;CT-FMT scan, create one subfolder whose name contains the mouse ID and the time point, e.g., M01_02h.</li>
	<li>For each &#181;CT-FMT scan, export the FMT scans (up and down) as .fmt files and save them into the subfolder using filenames ending with either &#8220;_up.fmt&#8221; or &#8220;_down.fmt&#8221;. Each .fmt file contains the acquired raw data, i.e., the excitation and emission images acquired by the camera, the metadata, such as the exposure times, and the fluorescence reconstruction generated by the FMT.</li>
	<li>Using the &#181;CT software, create a reconstruction with isotropic voxel size 35 &#181;m. Select a smooth reconstruction kernel (T10). Adjust the field of view so that the entire mouse bed including the markers is covered. Select MIFX/RAW as output format and start the reconstruction. After the reconstruction is done, move the &#181;CT reconstruction files into the subfolder of the &#181;CT-FMT scan.</li>
	<li>Export the &#181;CT and FMT data for all scans. Make sure that each subfolder contains two .fmt files (up and down) and the &#181;CT reconstruction in the MIFX/RAW format. To check for completeness select Menu-&#62;CT-FMT-&#62;Check Completeness using the Imalytics Preclinical software. A list of errors might appear, such as missing .fmt files or &#181;CT reconstructions. Fix the errors and check for completeness until all errors are resolved.</li>
	<li>Using Menu-&#62;CT-FMT-&#62;Settings, check the server name of the Definiens software and adjust if necessary. The default is http://localhost:8184, assuming that the Definiens software is installed on the same computer. The Definiens software is required in the next step to perform the automated segmentation of the mouse bed and markers.</li>
	<li>Click Menu-&#62;CT-FMT-&#62;Fuse group in the Imalytics Preclinical software to perform automated &#181;CT-FMT fusion for the entire study. This takes a few minutes per &#181;CT-FMT scan and results in a folder with suffix &#8220;Package&#8221; parallel to the study folder. This contains a smaller subset of files (the &#181;CT data and the fused vendor-provided FMT reconstruction) that are relevant for further analysis.</li>
	<li>Click Menu-&#62;CT-FMT-&#62;Reconstruct group (FMT) in the Imalytics Preclinical software to perform the fluorescence reconstruction including the generation of absorption and scattering maps13. Even though the processing is GPU-accelerated20, each reconstruction requires 1 to 4 hr depending on the size of the mouse. The results will be stored in the package folder. Note: To enable a higher throughput, we currently perform these reconstructions on a GPU cluster with 56 GPUs.</li>
</ol>
7. Image Analysis
NOTE: To extract quantitative measurements from the image data, segmentation of lesions and organs is required.
<ol>
	<li>Once all data sets are fused and reconstructed, create a segmentation for each &#181;CT-FMT scan using the Imalytics Preclinical software.</li>
	<li>Load a &#181;CT file as underlay and the fluorescence file as overlay. Press &#8220;3D&#8221; to turn on the volume rendering and inspect the data set.</li>
	<li>To segment the lung, click Menu-&#62;Classes-&#62;Add class and create a class named &#8220;tmp&#8221;. This can also be done through the context menu. Creating a new class automatically sets it as output class for subsequent segmentation operations.
	<ol>
		<li>Perform a thresholding operation to segment all regions with low intensity in the &#181;CT data set (click Menu-&#62;Segmentation-&#62;Thresholding-&#62;Below and enter 600). Now the tmp class contains the air outside the mouse but also the lung tissue.</li>
		<li>Create a &#8220;Lung&#8221; class. Perform a &#8220;Fill Region&#8221; operation (right-click into the lung and select Menu-&#62;Fill Region-&#62;Fill region unlimited), to separate the lung from the outer air.</li>
		<li>Delete the tmp class because it is not needed anymore.</li>
	</ol>
	</li>
	<li>To segment convex regions, e.g., the bladder, use the scribble mode. First create a class &#8220;Bladder&#8221;.
	<ol>
		<li>Press F1 to delete all scribbles.</li>
		<li>Using the computer mouse, draw scribbles to delineate the boundaries of the bladder.</li>
		<li>Press F3 to fill the region enclosed by the scribbles with a temporary mask which appears as red overlay. Iteratively add more scribbles (in any slicing orientation) and press F3 until a sufficient accuracy is achieved. Typically, scribbles in 10 slices are sufficient.</li>
		<li>Press F4 to store the temporary mask as &#8220;Bladder&#8221;.</li>
		<li>Proceed like this to segment other convex regions such as the heart and kidneys. Many regions, e.g., the stomach or the liver, can be approximated by a few convex regions.</li>
	</ol>
	</li>
	<li>To segment the spine, first create a &#8220;Bone&#8221; class.
	<ol>
		<li>Select ContextMenu-&#62;Thresholding-&#62;Above to perform a thresholding operation to classify all bright voxels (e.g., above 1,600) as &#8220;Bone&#8221;.</li>
		<li>A region filling operation would fail to segment the spine because it is connected with many other parts of the skeleton, e.g., the ribs. Perform a few cutting operations by iteratively drawing scribbles and pressing F2 to separate the spine from the skull, the ribs, and the sacral bone.</li>
		<li>Finally, create a class &#8220;Spine&#8221; and perform a region filling operation to get the spine (right-click into the spine and select ContextMenu-&#62;Fill Region-&#62;Fill region unlimited).</li>
	</ol>
	</li>
	<li>Save the segmentation as a file inside the subfolder of the &#181;CT-FMT scan. Use a consistent name, e.g.,&#160;organs.seg, to enable the batch processing.</li>
	<li>Select Menu-&#62;Statistics-&#62;Class statistics (overlay), to generate a spreadsheet which contains the mean fluorescence intensity, the volume and the total amount (product of mean and volume) for each class.</li>
	<li>To generate a single spreadsheet containing the values for all regions of all &#181;CT-FMT scans, click Menu-&#62;Batch-&#62;Set batch settings and then click Menu-&#62;Batch-&#62;Batch statistics. This avoids the effort of creating and merging many spreadsheet files, i.e., one for each &#181;CT-FMT scan.</li>
</ol>
8. Probe Calibration
<ol>
	<li>To calculate the calibration factor for a probe, multiple &#181;CT-FMT phantom scans with different known amounts of the probe are required (see step 1.4), e.g., with 100 pmol, 50 pmol, 25 pmol and 0 pmol.</li>
	<li>Scan the phantoms as described in sections 4 and 5. Also for phantom scans, up and down scans in the FMT are required.</li>
	<li>Export the data and perform fusion and reconstruction as described in section 6.</li>
	<li>Segment the inclusion using the &#181;CT data for each scan by thresholding (above 1,200) and region filling.</li>
	<li>Generate a spreadsheet with the measured fluorescence amounts and plot them as function of the known amounts. Compute the slope of a linear regression fit. This is the calibration factor for the probe.</li>
</ol>

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<li>Jacques, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Optical+properties+of+biological+tissues%3A+a+review%5BTitle%5D)+AND+%22Physics+in+Medicine+and+Biology%22%5BJournal%5D)">Optical properties of biological tissues: a review.</a> Physics in Medicine and Biology. 58 (11), R37(2013).</li>
<li>Loening, A. M., Gambhir, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((AMIDE%3A+a+free+software+tool+for+multimodality+medical+image+analysis%5BTitle%5D)+AND+%22Molecular+Imaging%22%5BJournal%5D)">AMIDE: a free software tool for multimodality medical image analysis.</a> Molecular Imaging. 2 (3), 131-137 (2003).</li>
<li>Gremse, F., Schulz, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Qualitative+and+Quantitative+Data+Analysis%5BTitle%5D)+AND+%22Small+Animal+Imaging%22%5BJournal%5D)">Qualitative and Quantitative Data Analysis.</a> Small Animal Imaging. , 363-378 (2011).</li>
<li>Baiker, M., Milles, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Atlas-based+whole-body+segmentation+of+mice+from+low-contrast+Micro-CT+data%5BTitle%5D)+AND+%22Medical+Image+Analysis%22%5BJournal%5D)">Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data.</a> Medical Image Analysis. 14 (6), 723-737 (2010).</li>
<li>Sevick-Muraca, E. M., Rasmussen, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Molecular+imaging+with+optics%3A+primer+and+case+for+near-infrared+fluorescence+techniques+in+personalized+medicine%5BTitle%5D)+AND+%22Journal+of+Biomedical+Optics%22%5BJournal%5D)">Molecular imaging with optics: primer and case for near-infrared fluorescence techniques in personalized medicine.</a> Journal of Biomedical Optics. 13 (4), 041303(2008).</li>
</ol>

Felix Gremse is founder and owner of Gremse-IT, a startup company that offers software and services for medical image analysis in co-operation with Philips and the Department for Experimental Molecular Imaging of the RWTH Aachen University.

We describe and apply a protocol for multimodal &#181;CT-FMT imaging. We use commercially available and widely used FMT and &#181;CT devices3,11,15&#8211;17,21. While the protocol requires a specific FMT, the &#181;CT can be replaced by another &#181;CT with similar functionality and comparable scanning parameters, e. g., the field of view should be large enough to cover the mouse bed including the markers.
The FMT has been used for biodistribution analysis without combining it with &#181;CT or MRI21, however, the anatomical data is beneficial to increase the reproducibility because the segmentation can be based on the organ boundaries which are visible in the &#181;CT data10. While integrated &#181;CT-FMT devices have been developed2,7, these are not commercially available yet. Furthermore, the use of two separate devices allows piping, i.e., the next mouse can be imaged in the &#181;CT while the first mouse is still in the FMT, to increase the throughput.
To reduce the manual workload, we perform automated marker detection and fusion. Furthermore, the mouse shape is automatically segmented and this information significantly improves the fluorescence reconstruction11,13,22. For quantitative fluorescence reconstruction, absorption and scattering maps are needed13,23. We derive the scattering map by automated segmentation of the &#181;CT data and assigning known scattering coefficients of several tissue types (lung, bone, skin, fat, and remaining soft tissue)24. Subsequently, we reconstruct an absorption map from the optical raw data which is particularly important for well-perfused organs such as the heart and the liver13,20.
Scanning several mice at multiple time points quickly results in a large number of data sets to be analyzed. For biodistribution studies, several organs need to be segmented for each &#181;CT-FMT scan. Unfortunately, the segmentations cannot be reused, because the mouse is newly positioned into the mouse bed repeatedly. We use a tool for interactive segmentation, developed at our institute, however, other tools might also be appropriate25. We generate voxel-wise segmentations, because these match better to complex organs than simple shapes such as ellipses and cubes26. Automated whole-animal segmentation would be useful to further reduce the manual workload27, but an interactive segmentation tool would still be required to correct for segmentation errors. Furthermore, automated segmentation tools can hardly anticipate special cases such as pathologies correctly. Since we use native &#181;CT scans, some organs such as the spleen are very difficult to segment even manually. Contrast agents would help, but there are problems with tolerability and it is difficult to maintain a steady contrast agent distribution throughout the longitudinal imaging.
Our phantom study shows that the signal localization is improved when using the shape information for fluorescence reconstruction. In vivo, a similar improvement is evident for the early time point (15 min&#160;after injection), when a large amount of the probe is already in the urinary bladder. The hydroxyapatite-binding probe accumulates at bones and joints. It is remarkable how fast this occurs, i.e., the signal is already clearly visible at the spine 15 min&#160;after injection. This probably is caused by the low molecular weight of the probe, which enables fast extravasation and diffusion to the target regions. The probe binds covalently to its target hydroxyapatite and the unbound probe is excreted. For the later time points, between 6 h and 24 h after injection, the signal intensity in the spine remains relatively stable, probably, because hardly any light reaches deep into the mouse to bleach the fluorescence. For our study, we used the 750 nm channel, which results in low background fluorescence as evident for the scans acquired before injection. At lower wavelengths, more background signal can be expected28.
In summary, we describe a multimodal imaging protocol for commercially available FMT and &#181;CT devices. We show that the combination provides benefits for fluorescence reconstruction. We illustrate how the biodistribution curves are extracted from the large amount of image data by means of interactive organ segmentation and batch processing. We believe that this standardized workflow can be helpful for drug development and other imaging studies using fluorescently labeled probes.

Fluorescence-mediated tomography, also called fluorescence molecular tomography (FMT), is a promising technique to quantitatively assess the fluorescence distribution in diffuse tissues, such as anesthetized mice or even human body tissues, e.g., breasts or finger joints. In contrast to non-invasive microscopy techniques, which allow imaging of superficial targets at subcellular resolution1, FMT allows three-dimensional reconstruction of fluorescent sources in depths of several centimeters, albeit at lower resolution2. Many targeted fluorescent probes are available to image angiogenesis, apoptosis, inflammation, and others2&#8211;5. Some probes are activatable, e.g., by specific enzyme cleavage leading to unquenching of fluorochromes. Moreover, reporter genes expressing fluorescent proteins can be imaged, e.g., to track tumor cell migration6.
FMT strongly benefits from the combination with an anatomical modality, e.g., &#181;CT2,7 or MRI8. While stand-alone FMT devices are commercially available9, the fluorescence images are difficult to interpret without anatomical reference information. Recently we were able to show, that the fused anatomical image data enables a more robust analysis10. The anatomical data can also be used to provide prior knowledge, such as the outer shape of the mouse, which is important for accurate optical modeling and fluorescence reconstruction11. Furthermore, optical scattering and absorption maps can be estimated using segmentation of tissue types and by assigning class specific coefficients12,13. For near-infrared light, hemoglobin is the main absorber in mice, besides melanin and fur14. Since the relative blood volume varies regionally by orders of magnitude, an absorption map is particularly important for quantitative fluorescence reconstruction13.
One advantage of using non-invasive imaging devices is that the mice can be imaged longitudinally, i.e., at multiple time points. This is important to assess the dynamic behavior of probes, i.e., their target accumulation, biodistribution and excretion10,15, or to assess the disease progression16. When imaging several mice at multiple time points, a large amount of image data sets arises. To enable comparability, these should be acquired in a systematic way, i.e., with a well-defined and documented protocol. The large number of scans poses a challenge for image analysis, which is required to extract quantitative measurements from the image data.
The purpose of our study is to provide a detailed description of a &#181;CT-FMT imaging protocol which we used and optimized throughout several studies10,13,15,17,18. We describe how the data sets are generated, processed, visualized, and analyzed. This is demonstrated using an established molecular probe, OsteoSense, that binds to hydroxyapatite19, and can be used to image bone diseases and remodeling2. All procedures involving animals were approved by the governmental review committee on animal care.

<table><tbody><tr><td>FMT (Fluorescence molecular tomography) FMT2500 LX</td><td>PerkinElmer</td><td>FMT2000</td><td>Device for fluorescence molecular tomography</td></tr><tr><td>&amp;micro;CT (micro computed tomography) Tomoscope Duo</td><td>CT Imaging GmbH</td><td>Tomoscope Duo</td><td>Device for micro computed tomography</td></tr><tr><td>Multimodal Mouse Bed</td><td>CT Imaging GmbH</td><td>Experimental builder</td><td>Partially transparent animal holder</td></tr><tr><td>IsoFlo (isoflurane, USP)</td><td>Abbott</td><td>05260-05</td><td>Isoflurane Inhalation anesthesia</td></tr><tr><td>Small animal anesthesia system</td><td>Harvard apparatus</td><td>726419</td><td>Complete Isoflurane Table-Top System</td></tr><tr><td>Chlorophyll-free mouse food</td><td>Ssniff</td><td>E15051</td><td>low chlorophyll / low fluorescence food</td></tr><tr><td>OsteoSense 750EX</td><td>PerkinElmer</td><td>NEV10053EX</td><td>Animal FMT contrast agent</td></tr><tr><td>Portex Fine Bore Polythene Tubing</td><td>Smith medical</td><td>800/100/120</td><td>Tube for injection catheter</td></tr><tr><td>Sterican 30g</td><td>BBraun</td><td>4656300</td><td>Hypodermic needle for catheter</td></tr><tr><td>Imeron</td><td>Altana pharma</td><td>INLA F.1/0203/3.5337.69</td><td>CT contrast agent for the phantom inclusions</td></tr><tr><td>Agarose</td><td>Sigma</td><td>90-12-36-6</td><td>Agarose for phantom production</td></tr><tr><td>TiO2</td><td>Applichem</td><td>A1900,1000</td><td>Titanium oxyde as phantom scattering agent</td></tr><tr><td>Trypan blue</td><td>Fluka</td><td>93595</td><td>Trypan blue to adjust phantom light propagation</td></tr><tr><td>Cy7</td><td>Lumiprobe</td><td>15020</td><td>Fluorochrome for the phantom inclusions</td></tr><tr><td>Lipovenoes 20%</td><td>Fresenius Kabi</td><td>3094740</td><td>Lipid emulsion, scattering agent for FMT contrast agents</td></tr><tr><td>Definiens Developer XD Server</td><td>Definiens AG</td><td>Server XD</td><td>Software platform for automated segmentation</td></tr><tr><td>Imalytics Preclinical</td><td>ExMI/Gremse-IT</td><td>Version 2.0.1</td><td>Software for image fusion, reconstruction and analysis</td></tr><tr><td>NVIDIA Geforce Titan</td><td>Asus</td><td>GTXTITAN6GD5</td><td>High end computer graphics card, 6GB Memory</td></tr></tbody></table>

hybrid &#181;ct-fmt imaging and image analysis

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (&#181;CT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal &#181;CT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints.

We applied the described protocol to assess the biodistribution of a targeted probe, OsteoSense, which binds to hydroxyapatite. 3 mice (C57BL/6 Apoe-/- Ahsg-/- double knockout mice, 10 weeks old) were imaged before and 15 min, 2 h, 4 h, 6 h, and 24 h after i.v. injection of 2 nmol OsteoSense. Our software automatically detected the markers built into the multimodal mouse bed (Figure&#160;1, Figure&#160;2A,B), which enabled fusion of the anatomical &#181;CT data with the fluorescence reconstruction performed by the FMT (Figure&#160;2C,D). Since OsteoSense is a probe with a low molecular weight, a fast renal excretion and therefore high signal in the urinary bladder is expected. Fusion of the fluorescence reconstruction of the FMT revealed problems such as misplaced signal outside the bladder (Figure 2C,D). These problems occur because the FMT does not know the true shape of the mouse and assumes a block shape. Our reconstruction determines the accurate shape from the &#181;CT data and generates scattering and absorption maps13 in order to enable a more accurate fluorescence reconstruction with better signal localization, which is particularly evident for the bladder (Figure&#160;2E,F).
To assign the reconstructed fluorescence to appropriate regions, we interactively segmented several organs using our software (Figure&#160;3). For each of the 18 scans, 7 regions were segmented based on the &#181;CT data, i.e., heart, lung, liver, kidneys, spine, intestine and bladder. Subsequently, the software was used to compute the mean fluorescence concentration for each of the 126 regions. Fortunately, the software provides a batch mode, which computes all the values and saves them in a single spreadsheet.
To visualize the fluorescence distribution, 3D renderings were generated for each time point, using comparable windowing setting&#160;(Figure&#160;4A-F). Using the quantified organ values, the biodistribution was computed by averaging the organ values over the three mice (Figure&#160;4G). The pre scans, acquired before injection, showed negligible background signal. 15 min&#160;after injection, the strongest signal appeared in the urinary bladder, because of the fast renal excretion. At the subsequent time points, the remaining probe had accumulated at bones and joints.
<img alt="Figure 1" src="/files/ftp_upload/52770/52770fig1.jpg" /> 
Figure 1.&#160;Multimodal Mouse Bed. (A) The multimodal mouse bed contains two acrylic glass plates that tightly hold the mouse. The tightening is adjusted using two screws. The mouse bed contains markers (empty holes) for image fusion. Anesthetic gas is supplied using a flexible tube which is fixated with tape. (B) The mouse bed is attached to a metal holder and held in the center of the rotating &#181;CT gantry. (C) Avoid a gap between mouse bed and the metal holder, because otherwise, the markers may be incorrectly assigned leading to incorrect fusion. The anesthetic gas tube should be attached to the tube connector. (D) The mouse bed should be inserted into the FMT with the front first to enable a correct automated fusion. (E) The markers are visible to the FMT camera, which is used for the automated marker detection and fusion. <a href="https://www.jove.com/files/ftp_upload/52770/52770fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52770/52770fig2.jpg" /> 
Figure 2. Image Fusion and Reconstruction. (A, B) Markers and the outer shape of the mouse are determined by the automated segmentation algorithm. (C, D) 15 min&#160;after injection of OsteoSense, a considerable amount of the probe has already been excreted into the urinary bladder. After fusing the vendor-provided reconstruction with the &#181;CT data, problems become visible. Most of the signal appears around the bladder but not inside the bladder and some signal even appears in the air. This happens because the FMT assumes a block-shaped mouse. (E, F) Our improved fluorescence reconstruction, using the shape of the mouse derived from the &#181;CT data, results in better localization of the fluorescence inside the bladder. <a href="https://www.jove.com/files/ftp_upload/52770/52770fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52770/52770fig3.jpg" /> 
Figure 3. Interactive Organ Segmentation. (A) To quantify the fluorescence distribution, several organs are segmented: heart (red), lung (pink), liver (brown), stomach (beige), spine (purple), kidneys (yellow), intestine (green), and urinary bladder (gold). (B) The lung, which is strongly contrasted compared with the surrounding tissue, is segmented using thresholding and region filling. (C) Round organs, such as the bladder, kidneys, and heart are segmented using &#8220;scribbles&#8221;. (D) Organs with a more complex shape, e.g., liver and stomach are segmented incrementally using scribbles. To segment the spine, a high threshold is applied to segment all bones. Then some bones, e.g., the ribs, are cut away, until the spine remains. <a href="https://www.jove.com/files/ftp_upload/52770/52770fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/52770/52770fig4.jpg" /> 
Figure 4. Biodistribution. To assess the biodistribution, the mice are scanned at several time points (A-F). (A) The pre scan, before injection, shows little background signal in the 750 nm channel. (B) 15 min&#160;after injection, a considerable amount of the probe is already in the urinary bladder. (C) At the 2 h time point, the mouse had urinated, which results in some fluorescence outside the mouse. At later time points (D-F), the signal appears predominantly at the bones and joints, i.e., at the spine and the knees. (G) The quantified fluorescence concentration is shown for selected organs.

Watch this Scientific Journal Video about Hybrid Ã‚ÂµCT-FMT imaging and image analysis at JoVE.com

Hybrid  &#181;CT-FMT imaging and image analysis

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

Hybrid &#181;CT-FMT imaging and image analysis

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

Research

JoVE Journal

Bioengineering

13.1K Views.  RWTH Aachen University. We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

Video: Hybrid  µCT-FMT imaging and image analysis

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring 

Nanomedications can be carried by blood borne monocyte-macrophages into the reticuloendothelial system (RES; spleen, liver, lymph nodes) and to end organs. The latter include the lung, RES, and brain and are operative during human immunodeficiency virus type one (HIV-1) infection. Macrophage entry into tissues is notable in areas of active HIV-1 replication and sites of inflammation. In order to assess the potential of macrophages as nanocarriers, superparamagnetic iron-oxide and/or drug laden particles coated with surfactants were parenterally injected into HIV-1 encephalitic mice. This was done to quantitatively assess particle and drug biodistribution. Magnetic resonance imaging (MRI) test results were validated by histological coregistration and enhanced image processing. End organ disease as typified by altered brain histology were assessed by MRI. The demonstration of robust migration of nanoformulations into areas of focal encephalitis provides '"proof of concept" for the use of advanced bioimaging techniques to monitor macrophage migration. Importantly, histopathological aberrations in brain correlate with bioimaging parameters making the general utility of MRI in studies of cell distribution in disease feasible. We posit that using such methods can provide a real time index of disease burden and therapeutic efficacy with translational potential to humans.

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Bioimaging methods used to assess cell biodistribution of nanoparticles are applicable for therapeutic and diagnostic monitoring of nanoformulated compounds. The methods described herein are sensitive and specific when assessed by histological coregistration. The methodologies provide a translational pathway from rodent to human applications.

Nanomedications can be carried by blood borne monocyte-macrophages into the reticuloendothelial system (RES; spleen, liver, lymph nodes) and to end ...

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM)

Scaling the number of measurable parameters, which allows for multidimensional data analysis and thus higher-confidence statistical results, has been the main trend in the advanced development of flow cytometry. Notably, adding high-resolution imaging capabilities allows for the complex morphological analysis of cellular/sub-cellular structures. This is not possible with standard flow cytometers. However, it is valuable for advancing our knowledge of cellular functions and can benefit life science research, clinical diagnostics, and environmental monitoring. Incorporating imaging capabilities into flow cytometry compromises the assay throughput, primarily due to the limitations on speed and sensitivity in the camera technologies. To overcome this speed or throughput challenge facing imaging flow cytometry while preserving the image quality, asymmetric-detection time-stretch optical microscopy (ATOM) has been demonstrated to enable high-contrast, single-cell imaging with sub-cellular resolution, at an imaging throughput as high as 100,000 cells/s. Based on the imaging concept of conventional time-stretch imaging, which relies on all-optical image encoding and retrieval through the use of ultrafast broadband laser pulses, ATOM further advances imaging performance by enhancing the image contrast of unlabeled/unstained cells. This is achieved by accessing the phase-gradient information of the cells, which is spectrally encoded into single-shot broadband pulses. Hence, ATOM is particularly advantageous in high-throughput measurements of single-cell morphology and texture &#8211; information indicative of cell types, states, and even functions. Ultimately, this could become a powerful imaging flow cytometry platform for the biophysical phenotyping of cells, complementing the current state-of-the-art biochemical-marker-based cellular assay. This work describes a protocol to establish the key modules of an ATOM system (from optical frontend to data processing and visualization backend), as well as the workflow of imaging flow cytometry based on ATOM, using human cells and micro-algae as the examples.

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

This protocol describes the implementation of an asymmetric-detection time-stretch optical microscopy system for single-cell imaging in ultrafast microfluidic flow and its applications in imaging flow cytometry.

Scaling the number of measurable parameters, which allows for multidimensional data analysis and thus higher-confidence statistical results, has been the ...

Engineering

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

With the desire to understand the contributions of multiple cellular elements to the development of a complex tissue; such as the numerous cell types that participate in regenerating tissue, tumor formation, or vasculogenesis, we devised a multi-colored cellular transplant model of tumor development in which cell populations originate from different fluorescently colored reporter gene mice and are transplanted, engrafted or injected in and around a developing tumor. These colored cells are then recruited and incorporated into the tumor stroma. In order to quantitatively assess bone marrow derived tumor stromal cells, we transplanted GFP expressing transgenic whole bone marrow into lethally irradiated RFP expressing mice as approved by IACUC. 0ovarian tumors that were orthotopically injected into the transplanted mice were excised 6-8 weeks post engraftment and analyzed for bone marrow marker of origin (GFP) as well as antibody markers to detect tumor associated stroma using multispectral imaging techniques. We then adapted a methodology we call MIMicc- Multispectral Interrogation of Multiplexed cellular compositions, using multispectral unmixing of fluoroprobes to quantitatively assess which labeled cell came from which starting populations (based on original reporter gene labels), and as our ability to unmix 4, 5, 6 or more spectra per slide increases, we&#39;ve added additional immunohistochemistry associated with cell lineages or differentiation to increase precision. Utilizing software to detect co-localized multiplexed-fluorescent signals, tumor stromal populations can be traced, enumerated and characterized based on marker staining.1

Complex tissue masses, from organs to tumors, are composed of various cellular elements. We elucidated the contribution of cellular phenotypes within a tissue utilizing multi-labeled fluorescent transgenic mice in combination with multiparameter immunofluorescent staining followed by spectral unmixing to decipher cell origin as well as cell characteristics based on protein expression.

With the desire to understand the contributions of multiple cellular elements to the development of a complex tissue; such as the numerous cell types that ...

Medicine

An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

The possibility to perform an early and repeatable assessment of imaging performance is fundamental in the design and development process of new ultrasound (US) probes. Particularly, a more realistic analysis with application-specific imaging targets can be extremely valuable to assess the expected performance of US probes in their potential clinical field of application.
The experimental protocol presented in this work was purposely designed to provide an application-specific assessment procedure for newly-developed US probe prototypes based on Capacitive Micromachined Ultrasonic Transducer (CMUT) technology in relation to brain imaging.
The protocol combines the use of a bovine brain fixed in formalin as the imaging target, which ensures both realism and repeatability of the described procedures, and of neuronavigation techniques borrowed from neurosurgery. The US probe is in fact connected to a motion tracking system which acquires position data and enables the superposition of US images to reference Magnetic Resonance (MR) images of the brain. This provides a means for human experts to perform a visual qualitative assessment of the US probe imaging performance and to compare acquisitions made with different probes. Moreover, the protocol relies on the use of a complete and open research and development system for US image acquisition, i.e. the Ultrasound Advanced Open Platform (ULA-OP) scanner.
The manuscript describes in detail the instruments and procedures involved in the protocol, in particular for&#160;the calibration, image acquisition and registration of US and MR images. The obtained results prove the effectiveness of the overall protocol presented, which is entirely open (within the limits of the instrumentation involved), repeatable, and covers the entire set of acquisition and processing activities for US images.

The development of new ultrasound (US) probes based on Capacitive Micromachined Ultrasonic Transducer (CMUT) technology requires an early realistic assessment of imaging capabilities. We describe a repeatable experimental protocol for US image acquisition and comparison with magnetic resonance images, using an ex vivo bovine brain as an imaging target.

The possibility to perform an early and repeatable assessment of imaging performance is fundamental in the design and development process of new ...

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry

The in vitro micronucleus (MN) assay is often used to evaluate cytotoxicity and genotoxicity but scoring the assay via manual microscopy is laborious and introduces uncertainty in results due to variability between scorers. To remedy this, automated slide-scanning microscopy as well as conventional flow cytometry methods have been introduced in an attempt to remove scorer bias and improve throughput. However, these methods have their own inherent limitations such as inability to visualize the cytoplasm of the cell and the lack of visual MN verification or image data storage with flow cytometry. Multispectral Imaging Flow Cytometry (MIFC) has the potential to overcome these limitations. MIFC combines the high resolution fluorescent imagery of microscopy with the statistical robustness and speed of conventional flow cytometry. In addition, all collected imagery can be stored in dose-specific files. This paper describes the protocol developed to perform a fully automated version of the MN assay on MIFC. Human lymphoblastoid TK6 cells were enlarged using a hypotonic solution (75 mM KCl), fixed with 4% formalin and the nuclear content was stained with Hoechst 33342. All samples were run in suspension on the MIFC, permitting acquisition of high resolution images of all key events required for the assay (e.g. binucleated cells with and without MN as well as mononucleated and polynucleated cells). Images were automatically identified, categorized and enumerated in the MIFC data analysis software, allowing for automated scoring of both cytotoxicity and genotoxicity. Results demonstrate that using MIFC to perform the in vitro MN assay allows statistically significant increases in MN frequency to be detected at several different levels of cytotoxicity when compared to solvent controls following exposure of TK6 cells to Mitomycin C and Colchicine, and that no significant increases in MN frequency are observed following exposure to Mannitol.

The in vitro micronucleus assay is a well-established method for evaluating genotoxicity and cytotoxicity but scoring the assay using manual microscopy is laborious and suffers from subjectivity and inter-scorer variability. This paper describes the protocol developed to perform a fully automated version of the assay using multispectral imaging flow cytometry.

The in vitro micronucleus (MN) assay is often used to evaluate cytotoxicity and genotoxicity but scoring the assay via manual microscopy is laborious and ...

Use of Micro X-ray Computed Tomography with Phosphotungstic Acid Preparation to Visualize Human Fibromuscular Tissue

Manual dissection and histological observation are common methods used to investigate human tissues. However, manual dissection can damage delicate structures&#160;while processing and histological observation provide limited information through cross-sectional imaging. Micro X-ray computed tomography (microCT) is an effective tool for obtaining three-dimensional information without damaging specimens. However, it shows limited efficiency in differentiating soft tissue parts. Use of contrast-enhancing agents, like phosphotungstic acid (PTA), can solve this problem by improving soft tissue contrast. We implemented microCT with PTA to investigate the human orbicularis retaining ligament (ORL), which is a delicate structure in the orbit area. In this method, harvested specimens are fixed in formalin, dehydrated in serial ethanol solutions, and stained with a PTA solution. After staining, microCT scanning, 3D reconstruction, and analysis are performed. Skin, ligaments, and muscles can be clearly visualized using this method. The specimen size and duration of staining are essential features of the method. The suitable specimen thickness was about 5&#8211;7 mm, above which the process was slowed, and the optimum duration was 5&#8211;7 days, below which an empty hole in the central area occasionally occurred. To maintain the location and direction of small pieces during cutting, sewing on the same region of each part is recommended. Furthermore, preliminary analyses of the anatomical structure are needed to correctly identify each piece. Parafilm can be used to prevent drying, but care should be taken to prevent specimen distortion. Our multidirectional observation showed that the ORL is composed of a multilayered meshwork of continuous plates, rather than thread-like fibers, as reported previously. These results suggest that microCT scanning with PTA is useful for examining specific compartments within complex structures of human tissue. It may be helpful in the analyses of cancer tissues, nerve tissues, and various organs, like the heart and liver.

Micro X-ray computed tomography is effective in obtaining three-dimensional information from undamaged human specimens but has limited success in observing soft tissues. The use of phosphotungstic acid contrast agent can resolve this issue. We implemented this contrast agent to examine human delicate fibromuscular tissues (the orbicularis retaining ligament).

Manual dissection and histological observation are common methods used to investigate human tissues. However, manual dissection can damage delicate ...

Effective Co-Registration of Two Preclinical Imaging Modalities Across Devices

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Integrated preclinical multimodal imaging systems, such as X-ray computed tomography (CT) combined with positron emission tomography (PET) or magnetic resonance imaging (MRI) combined with PET, are widely available and typically provide robustly co-registered volumes. However, separate devices are often needed to combine a standalone MRI with an existing PET-CT or to incorporate additional data from optical tomography or high-resolution X-ray microtomography. This necessitates image co-registration, which involves complex aspects such as multimodal mouse bed design, fiducial marker inclusion, image reconstruction, and software-based image fusion. Fiducial markers often pose problems for in vivo data due to dynamic range issues, limitations on the imaging field of view, difficulties in marker placement, or marker signal loss over time (e.g., from drying or decay). These challenges must be understood and addressed by each research group requiring image co-registration, resulting in repeated efforts, as the relevant details are rarely described in existing publications.
This protocol outlines a general workflow that overcomes these issues. Although a differential transformation is initially created using fiducial markers or visual structures, such markers are not required in production scans. The requirements for the volume data and the metadata generated by the reconstruction software are detailed. The discussion covers achieving and verifying requirements separately for each modality. A phantom-based approach is described to generate a differential transformation between the coordinate systems of two imaging modalities. This method showcases how to co-register production scans without fiducial markers. Each step is illustrated using available software, with recommendations for commercially available phantoms. The feasibility of this approach with different combinations of imaging modalities installed at various sites is showcased.

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.

Integrated preclinical multimodal imaging systems, such as X-ray computed tomography (CT) combined with positron emission tomography (PET) or magnetic ...

4D Ultrasound and MALDI Imaging for Cardiac and Vascular Analysis in Mice

Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging

Cardiovascular disease (CVD) is the leading cause of death in the United States. Damage in&#160;the cardiovascular system can be due to environmental exposure, trauma, drug toxicity, or numerous other factors.&#160;As a result, cardiac tissue and vasculature undergo structural changes and display diminished function. The damage and the resulting remodeling can be detected and quantified with ultrasound (US) imaging at the organ level and mass spectrometry imaging (MSI) at the molecular level. This manuscript describes an innovative methodology for studying murine cardiac pathophysiology, coupling in vivo four-dimensional (4D) ultrasound imaging and analysis with ex vivo matrix-assisted laser desorption/ionization (MADLI) MSI of the heart. 4D ultrasound can provide dynamic volumetric measurements, including radial displacement, surface area strain, and longitudinal strain throughout an entire cardiac cycle. In the vasculature, MSI and ultrasound are used to assess vessel wall compositions, hemodynamics, and vessel wall dynamics. The methodology can be tailored to study a myriad of CV diseases by adjusting functional metrics of interest and/or varying MALDI MSI protocol to target specific molecules. MALDI MSI can be used to study lipids, small metabolites, peptides, and glycans. This protocol outlines the use of MALDI MSI for untargeted lipidomic analysis and the use of ultrasound imaging for cardiovascular hemodynamics and biomechanics.

Here, we describe a protocol to use in vivo four-dimensional ultrasound imaging and ex vivo mass spectrometry imaging to assess biomechanical and biomolecular alterations in the murine cardiovascular system. This technique is applied to analyze cardiac remodeling in surgically induced myocardial infarction and vascular changes in aging animals.

Cardiovascular disease (CVD) is the leading cause of death in the United States. Damage in&#160;the cardiovascular system can be due to environmental ...

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Positron emission tomography (PET) and computed tomography (CT) are among the most employed diagnostic imaging techniques, and both serve in understanding cardiac function and metabolism. In preclinical research, dedicated scanners with high sensitivity and high spatio-temporal resolution are employed, designed to cope with the demanding technological requirements posed by the small heart size and very high heart rates of mice and rats. In this paper, a bimodal cardiac PET/CT imaging protocol for experimental mouse and/or rat models of cardiac diseases is described, from animal preparation and image acquisition and reconstruction to image processing and visualization.
In particular, the 18F-labeled fluorodeoxyglucose ([18F]FDG)-PET scan allows for the measurement and visualization of glucose metabolism in the different segments of the left ventricle (LV). Polar maps are convenient tools to display this information. The CT part consists of a time-resolved 3D reconstruction of the entire heart (4D-CT) using retrospective gating without electrocardiography (ECG) leads, allowing the morphofunctional evaluation of the LV and the subsequent quantification of the most important cardiac function parameters, such as ejection fraction (EF) and stroke volume (SV). Using an integrated PET/CT scanner, this protocol can be executed within the same anesthesia induction without the need to reposition the animal between different scanners. Hence, PET/CT can be seen as a comprehensive tool for the morphofunctional and metabolic evaluation of the heart in several small animal models of cardiac diseases.

Here, we present an experimental imaging protocol for the quantification of cardiac function and morphology using high-resolution positron emission tomography/computed tomography for small animals. Both mice and rats are considered, discussing the different requirements of computed tomography contrast agents for the two species.

Positron emission tomography (PET) and computed tomography (CT) are among the most employed diagnostic imaging techniques, and both serve in understanding ...

Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans

With 6 years of experience in implementing virtopsy routinely into the Hong Kong cetacean stranding response program, standardized virtopsy procedures, postmortem computed tomography (PMCT) acquisition, postprocessing, and evaluation were successfully established. In this pioneer cetacean virtopsy stranding response program, PMCT was performed on 193 stranded cetaceans, providing postmortem findings to aid necropsy and shed light on the biological health and profile of the animals. This study aimed to assess 8 image rendering techniques in PMCT, including multiplanar reconstruction, curved planar reformation, maximum intensity projection, minimum intensity projection, direct volume rendering, segmentation, transfer function, and perspective volume rendering. Illustrated with practical examples, these techniques were able to identify most of the PM findings in stranded cetaceans and served as a tool to investigate their biological health and profile. This study could guide radiologists, clinicians and veterinarians through the often difficult and complicated realm of PMCT image rendering and reviewing.

The Hong Kong cetacean stranding response program has incorporated postmortem computed tomography, which provides valuable information on the biological health and profile of the deceased animals. This study describes 8 image rendering techniques that are essential for the identification and visualization of postmortem findings in stranded cetaceans, which will help clinicians, veterinarians and stranding response personnel worldwide to fully utilize the radiological modality.

With 6 years of experience in implementing virtopsy routinely into the Hong Kong cetacean stranding response program, standardized virtopsy procedures, ...

Hybrid µCT-FMT imaging and image analysis

Summary

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